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Most existing AMBTC (absolute moment block truncation coding)-based reversible data hiding (RDH) schemes cannot be decoded by traditional AMBTC decoders, because they change the coding structure of the AMBTC compressed code stream, which is unidentified for decoders. Even if some AMBTC-based RDH methods can be decoded by traditional AMBTC decoders, but the obtained payload is low. To this end, in this paper, a high-capacity and decodable AMBTC-based RDH scheme is presented. It is well observed that an $m\times n$ -sized bitmap has $2^{m\times n}$ combinations of ‘1’ and ‘0’. However, all the bitmaps of an image occupy only a small part of all the combinations, and a large part are not used in this image. Motivated by this observation, we propose a one-to-many map between the used and unused combinations, in which one used bitmap can be mapped to multiple unused combinations, achieving high payloads (e.g., $\log _{2} 25$ bits for one bitmap of Lena), and more importantly, completely reserving the coding structure of the AMBTC-compressed code. Unlike existing decodable AMBTC-based RDH methods only capable of achieving a fixed payload for one test image, our method can adjust adaptively payloads according to the required visual quality by introducing a predefined threshold. The experimental results also demonstrate that our proposed scheme can largely increase the payload on the basis of maintaining good visual quality.

Digital microfluidic biochips (DMFBs) are increasingly important and are used for point-of-care, drug discovery, clinical diagnosis, immunoassays, etc. Pin-constrained DMFBs are an important part of digital microfluidic biochips, and they have gained increasing attention from researchers. However, many previous works have focused on the problem of electrode addressing and aimed to minimize the number of control pins in pin-constrained DMFBs. Although the number of control pins can be effectively redistributed through broadcast addressing technology, the chip reliability will be reduced if the signals are shared arbitrarily. Arbitrary signal sharing can lead to a large number of actuations for many idle electrodes, and as a result, a trapping charge or decreasing contact angle problem could occur for some electrodes, reducing the reliability of the chip. To address this problem, the appropriate electrode matching object should be carefully selected, and the influence of these factors on chip reliability should be fully considered. For this purpose, we aimed to fully consider electrode addressing and the reliability of the chip in improving the reliability of DMFBs. This paper proposed a pin addressing method based on a support vector machine (SVM) with the reliability constraint algorithm, which can fully consider the electrode addressing method and the reliability of the chip together. The proposed method achieved an average maximum number of electrode actuations that was 53.8% and 18.2% smaller than those of the baseline algorithm and the graph-based algorithm, respectively. The simulation experiment results showed that the proposed method can efficiently solve reliability problems during the DMFB design process.

Smart Campus is an intelligent and smart environment of teaching, learning and living. It is built on the Internet of Things and application services. Colleges and universities are devoted to the construction and development of the smart campus and have completed some digital campuses of different sizes. However, there are still some problems in the current construction and development process, such as low degree of intelligence, free intelligent functions, energy and resource monitoring is still in the traditional mode of non-intelligent management, the functions of terminal application are still simple, can’t keep up with the intelligent development of the campus. In order to help promote the development of smart campus, we designed a database, smart campus mobile terminal APP. The smart campus APP is based on Android system to monitor and control energy and other nodes in the campus. The monitoring method is real-time, clear and efficient. In addition, smart dormitory, smart classroom and smart map function have also been designed to provide a more convenient and safe living environment for teachers and students in our school. And this APP has laid a solid foundation for establishing a smart, efficient, open, harmonious, humane and safe campus.

Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.

Aspect-based sentiment classification is a fine-grained task in sentiment analysis, it aims to detect the sentiment polarity of an aspect in a given context. Existing approaches mostly focused on the processing of aspect words, which often fail to adequately model the context via aspect terms. However, the surrounding words in context usually have an important impact on the sentiment polarity of aspect words. This paper proposes a context enhanced attention network (CE-ATT) to enhance context modeling for aspect-based sentiment classification. Firstly, the text sequence was divided into left and right contexts by a given aspect. Then, the Bidirectional Long Short Term Memory (BiLSTM) was used to model the left context and aspect, the right context and aspect, and both sides context and aspect, respectively. After summing these contextual representations, an attention mechanism was used to concentrate on the parts that have an important influence on the aspect word. The experimental results on three benchmark datasets demonstrate the effectiveness of the CE-ATT method.

The traditional calibration method for charging piles which used for electric vehicles relies on the field inspection of standard devices of metrology agency. However, with the continuous expansion of electric vehicles' scale in the future, the calibration workload of charging piles will become larger and larger, calibration method that relies solely on the standard devices cannot hold so heavy workload. In this paper, we propose a new calibration method, which can reduce the calibration workload of standard devices fundamentally. Firstly, we train a model which called virtual standard device for the metrology standard through machine learning, and when calibrating the charging pile, we just need to input the charging message into the model, and then we can get the calibration result which includes normal pile, abnormal pile and uncertain pile. Once the model training is completed, the entire calibrating process including the collection of message data, the import of data into the model and the calibration result requires no human intervention unless the result is uncertain. Moreover, it is found that the higher the model accuracy is, the smaller the uncertainty interval is and the less the manual calibration work is. The method of simulated calibration plus field calibration proposed in this paper can help reduce the calibration workload fundamentally and greatly reduce the calibrating pressure of metrology agency.

It is highly important to finish the experiments while learning the course of digital circuit design is to confirm the theoretical knowledge in the experiment, which makes the learning process an extremely good expansion. However, in the actual situation, digital design laboratories are often in short supply, which is mainly reflected in its spatial limitations, the limitations on the number of instruments and instrument stability limitations, especially in some poor and developing countries. This paper designed a mobile virtual laboratory application (VL-APP) for learning digital design to help students perform virtual experiments to conduct new thoughts anywhere and anytime, which also support anyone familiar with the instruments used in the digital area. It mainly aims at the simulation experiment of a specific experiment in the digital design experiment, and provides the model and script basis for the users to design the experiment independently. Selecting Unity3D and 3ds-max for the software platform, Android for the test installation environment, the APP testing results showed that it is good enough to be applied right now.

The state of health (SOH) is a key parameter for fault diagnoses and safety early warnings in the life cycle of lithium batteries in electric vehicles. The SOH prediction model generally uses the experimental data from the same batch of batteries in the same environment. These data may cause “overfitting” to the model as the attenuation of lithium batteries varies depending on the batch and working condition, especially in actual use. And there is a risk of serious deviation in the prediction result if there is no true value of the model. This paper proposes a SOH prediction model that evaluates the prediction uncertainty using data from different batches of batteries under actual working conditions. It not only quantitatively evaluates the credibility of the prediction model in absence of true values, but also filtering training data to improve the model accuracy and avoid overfitting. The model produces evaluation uncertainty for the prediction result based on the Gaussian process regression (GPR) method. Experiments' results show that the evaluation uncertainty is better than the prediction variance of GPR. The accuracy of the prediction model using the minimum evaluation uncertainty as the training data screening is an order of magnitude higher than that using all data for training.

Digital microfluidic biochips (DMFBs) are increasingly important used for clinical diagnostics drug discovery and point-of-care. Those procedures require high output precision, so the reliability and lifetime of the chips are extremely important. Due to the inherently reconfigurable nature of DMFBs, a degraded electrode may be reused many times. Therefore, the lifetime of the chips is closely related to the total actuation time of an electrode. Thus, the electrode total actuation time needs to be considered carefully in an efficient DMFB design process. This paper proposes an improved whale optimization algorithm (IWOA), which can reduce the excessive use of an electrode and reuse electrodes in an average manner to optimize the longest lifetime of DMFBs. First, the IWOA combined a WOA with a genetic algorithm, and inertial weights were added to enhance the local search ability of the IWOA. Second, the max-min WOA was proposed in the exploration phase. Lastly, the position mass of individual whales was improved to represent the sequence of operations. The simulation experimental results showed that this algorithm was able to solve lifetime optimization problems. The efficiency and convergence performance of the algorithm were very good. The proposed algorithm can achieve maximum improvement in the maximum electrode activation time of 74.6%, 8.2%,15.7% and 7.2% respectively, compared with T-trees algorithm, 3-DDD algorithm, PRSA algorithm and 3D-DDMS algorithm.

In the paper, the concept of logical stochastic resonance is applied to implement logic operation and latch operation in time-delayed synthetic genetic networks derived from a bacteriophage λ. Clear logic operation and latch operation can be obtained when the network is tuned by modulated periodic force and time-delay. In contrast with the previous synthetic genetic networks based on logical stochastic resonance, the proposed system has two advantages. On one hand, adding modulated periodic force to the background noise can increase the length of the optimal noise plateau of obtaining desired logic response and make the system adapt to varying noise intensity. On the other hand, tuning time-delay can extend the optimal noise plateau to larger range. The result provides possible help for designing new genetic regulatory networks paradigm based on logical stochastic resonance.

For the open laboratory management of an university, the attendance data is not accurate, and meanwhile, the statistics data is too easy to be lacked by using the traditional devices. Wi-Fi probe for people flow detection is often used for safety warnings in public places and commercial promotion of products, laboratory attendance and so on. However, the existing technologies used for laboratory attendance only identify the presence of a target while neglect the fact that when the coverage range of Wi-Fi signal exceeds the attendance range, the attendance data obtained by Wi-Fi probe will be inaccurate. So far, Wi-Fi probe has not been applied for managing people flow of open laboratory. In this paper, we propose an algorithm based on Wi-Fi probe so as to successfully increase the quality of people flow detection in open laboratory. We use RSSI positioning theory to distinguish people inside and outside the laboratory and identify the statistics of the people flow. The experimental results also demonstrate our system not only has higher accuracy than the existing RFID attendance systems, but also provide the rank of time which people spent in the laboratory, the numbers of people and the total time which all the people spend in the lab in a certain time. Managers and users can log in the system to check the statistical data. The system improves the utilization of the laboratory and contributes to the development of open laboratory management practices.

This paper presents an enhanced sparse based discriminative topic representation method for text categorization. By constructing category center vectors and combining with the latent Dirichlet allocation model, a more discriminative dictionary is obtained, which can describe the relationship between topic and word well. Furthermore, an enhanced sparse representation of documents can be generated with a $L_{1/2}$ regularization in order to achive a good relationship between document and topic. The experimental results show that our proposed approach achieves more stable classification performance and obtains more high sparse degree.

Collaborative deep learning can provide high learning accuracy even participanted users' datasets are small. In the training process, users only share their locally obtained parameters, therefore it is believed that the privacy of users' original datasets can be protected. However, we present an attack approach against users' privacy in collaborative deep learning by utilizing Generative Adversarial Network (GAN) and Membership Inference. In this attack, an attacker builds a discriminator based on users' shared parameters and then trains a GAN network locally. The GAN can refactor the training records of the collaborative deep learning system. According to the generated records, the attacker uses the extent of model overfitting on an input and gets the membership of each group of records by the simplified Membership Inference attack. We evaluate the presented attack model over datasets of complex representations of handwritten digits (MINIST) and face images (CelebA). The results show that an attacker can easily generate the original training sets and classify them to obtain the membership between users' records and their identities in the collaborative deep learning.

The large volume of event information makes it difficult for users to find interesting events in social networks. Therefore, we would like to develop an intelligent event recommendation to reduce information overload. Specifically, by exploring the behavior of users during the selection process, we are able to find particular rules associated with various event attributes which reflect the willingness of users. However, traditional event recommendations in social networks mainly concern the basic items like time and location. It is noted that few studies have yielded specific aspects such as the influence and spread capability of events and hosts. In this paper, we propose an event recommender approach fusing multiple features that can provide users with customized contents. To be specific, we consider hybridizing features including event influence, host impact, fee, social relationship and spatiotemporal characteristics. In order to achieve better performance, we concern the match degree between user and event properties especially in terms of their content and impact. Based on the improved idea of RankNet with neural networks, we build a Learning to Rank algorithm to reveal the importance of each feature. We rectify the problem of data sparse and cold start to grasp the balance of accuracy and novelty. Extensive experiments on datasets demonstrate that our method achieves promising results in comparison with other schemes.

Digital microfluidic biochips (DMFBs) are attractive instruments for obtaining modern molecular biology and chemical measurements. Due to the increasingly complex measurements carried out on a DMFB, such chips are more prone to failure. To compensate for the shortcomings of the module-based DMFB, this paper proposes a routing-based fault repair method. The routing-based synthesis methodology ensures a much higher chip utilization factor by removing the virtual modules on the chip, as well as removing the extra electrodes needed as guard cells. In this paper, the routing problem is identified as a dynamic path-planning problem and mixed path design problem under certain constraints, and an improved Dijkstra and improved particle swarm optimization (ID-IPSO) algorithm is proposed. By introducing a cost function into the Dijkstra algorithm, the path-planning problem under dynamic obstacles is solved, and the problem of mixed path design is solved by redefining the position and velocity vectors of the particle swarm optimization. The ID-IPSO routing-based fault repair method is applied to a multibody fluid detection experiment. The proposed design method has a stronger optimization ability than the greedy algorithm. The algorithm is applied to , , and fault-free chips. The proposed ID-IPSO routing-based chip design method saves 13.9%, 14.3%, and 14.5% of the experiment completion time compared with the greedy algorithm. Compared with a modular fault repair method based on the genetic algorithm, the ID-IPSO routing-based fault repair method has greater advantages and can save 39.3% of the completion time on average in the completion of complex experiments. When the ratio of faulty electrodes is less than 12% and 23%, the modular and ID-IPSO routing-based fault repair methods, respectively, can guarantee a 100% failure repair rate. The utilization rate of the electrodes is 18% higher than that of the modular method, and the average electrode usage time is 17%. Therefore, the ID-IPSO routing-based fault repair method can accommodate more faulty electrodes for the same fault repair rate, the experiment completion time is shorter, the average number of electrodes is lower, and the security performance is better.

The available capacity of a lithium battery reflects its actual capacity under certain constraints. It serves as an important deciding factor for the electric vehicles’ energy management system. Online estimation allows the construction of a mathematical model with easily measurable variables as input to estimate the main variables that are difficult to measure in a complex environment. Current research applies the trained model of another battery to the current testing battery. Due to differences across batteries, the same model is unable to deliver an accurate description of the chemical processes of the same type of batteries. In order to solve the problem of model adaptability, an iterative EKF-GPR method is proposed based on the full charging curve of the battery being estimated and the daily charging voltage, current and time data based on the assumption that there are no abrupt changes in the lithium battery charging curves. The method is to establish a model for the estimation of the available current charging capacity of the lithium battery. It converts macro-time machine learning into the prediction and estimation of single charging curve on the micro-time scale, thus guaranteeing the accuracy of available capacity estimation models for any batch of batteries.

The use of emergency communication technology in earthquake emergency communications was studied in this paper. Based on the existing emergency communication technology of Fujian Seismological Bureau, the paper puts forward a solution to meet the interconnection, voice interworking and data sharing between maritime emergency vessel and shore command. The program is implemented in the real-time monitoring project of the marine geophysical platform in the exploration of the deep structure of the crust in the western part of the Taiwan Strait, Which examines the feasibility of the program. The system has made a try in the field of marine seismic observation and communication, which has filled the gap of maritime earthquake emergency information interaction ability.

In text categorization, feature representation for dimensionality reduction is a key step. Usually, some commonly used methods, e.g., latent semantic analysis (LSA), yield a dense representation or a dense transformation matrix, which is difficult to precisely characterize the document-topic or the topic-word relationship. This paper proposes a novel discriminative topic sparse representation (DTSR) approach for text categorization, in which two stages are included: the topic dictionary construction and sparse representation. Firstly, a discriminative and interpretable dictionary is constructed to characterize the topic-word relationship. The dictionary contains all category center vectors as well as some semantic topic vectors generated by a latent Dirichlet allocation (LDA) model. Furthermore, each document can be represented with a sparse form to obtain a good document-topic relationship. Experimental results on well-known benchmark datasets indicate that the proposed method not only achieves a satisfactory classification performance but also provides a reasonable sparse semantic meaningful.

Some noisy nonlinear systems could be exploited to operate reliable logic operation in an optimal window of noise intensity, which is termed as logical stochastic resonance (LSR). We investigated the LSR phenomenon in bistable systems when internal noise and external noise are correlated. The LSR effect is evaluated by the success probability of the obtained desired output with various combinations of logic inputs. It is shown that the or-nor, and-nand, and Latch operations still can operate reliably with the correlated internal noise and external noise. A positive correlation strength tends to enhance or-nor logic and suppress and-nand logic. The negative correlation strength tends to suppress or-nor logic and enhance and-nand logic. The results provide possible corroboration for implementing reliable LSR when internal noise and external noise are correlated.

The grouped structure has successfully been embedded in sparse models for feature selection; however, some groups generated by clustering method might be difficult to interpret their semantic information if the number of words in the group is very large. This paper proposes a novel approach in which a group structure is constructed and its corresponding sparse model is used to select features for text categorization. After variable preselection, an algorithm is developed to generate groups, such that each group only contains two or three closely related words, which can reflect more essential semantic meaningful. Finally, structural sparse mode is used to select feature in wrapper way. The experimental results demonstrate that the proposed method achieves comparable precision and improves the sparsity considerably, which means that the model has better interpretability.

With the addition of online laboratories, distance education can become more meaningful and complete. Virtual laboratory is a high efficient tool for practical teaching assisted instruction. This paper design the virtual laboratory. Android application structure after analyzing the characteristics and the classification of virtual laboratory. It discusses the construction and system structure of network-based virtual laboratory which is used for MOOC (massive open online courses). This virtual laboratory environment goes based on conventional computer simulations and real cellphone test, which could provide the students with the flexibility to perform various experiments related to the fundamental law of physics, chemistry and et all. In this virtual laboratory APP, no matter the students or the instructor are represented as virtual characters. Assessment tools such as preview and review experiment tests are part of the laboratory environment which could provide different levels of the students at every step of the laboratory exercises. This paper presents a virtual lab established through data socket technology that enables the end user to design and implement control algorithms. Although this makes the problem more challenging, the quality of learning is also enhanced enormously.

Serial bus protocol has become popular and wide-used, and in many cases, it is necessary but hard to find a simple way to analyze its communication process due to its serial features. In this paper, a data acquisition based method is proposed to analyze serial bus communication with standalone clock signals. This method is tested with SPI and I2C bus data and gives the correct results. The method is proven to analyze this kind of serial protocols by translating the serial bus data into dedicated contents according to the protocol format, which extends the utility of instruments.

To effectively resolve the automatic recognition of analog pointer measuring instruments, a reading recognition method based on Hough transform is proposed. In order to simplify the following procedure, image graying and binary, mathematical morphology, including dilation and thinning, edge recognition was applied to the system. We proposed the recognition principle for reading recognition of the pointer's angle by using Hough transform. And the reading of the pointer is decided by the linear relationship between the panel's scale and the angle of the instrument's dial revolutionary. Considering that in most practical cases, the panel cannot be horizontal, so the procedure of tilt correction is employed in this paper. Finally, the software is written in MATLAB. The recognition system is achieved in LabVIEW, where the front panel of the reading system is developed. The experimental results demonstrate that our method is feasible for pointer instrument recognition.

The combination of spectroscopy and a novel classification algorithm called extreme learning machine (ELM) was developed for food classification. The performance influence of different input dimensionalities was also investigated. The classification accuracy and speed of five chemometrics techniques, including k-nearest neighbor (KNN), partial least-squares discriminant analysis (PLS-DA), back propagation artificial neural network (BP-ANN), least-squares support vector machine (LS-SVM), and ELM were compared. It was presented that the accuracy of ELM was better than its competitors in most cases. Moreover, on the classification stage, ELM performed much faster than KNN, LS-SVM, and BP-ANN, which indicated that ELM may be a promising method for real-time food classification with a comparable accuracy based on near or mid-infrared spectroscopy.

One of the most important issues in chemometrics is how to design a robust classifier for spectra data with high classification accuracy. Extreme learning machine (ELM) has proved to be an effective method for numerous pattern recognition problems. However, the randomness of ELM’s hidden layer nodes setting would give rise to the oscillation of the hidden layer output matrix, which may bring down the stability of this model. This paper proposes a framework for spectra data classification using the kernel extreme learning machine (KELM) that can circumvent the calculation of hidden layer outputs and encode it in a kernel matrix inherently. Moreover, KELM doesn’t need to specify the hidden layer feature mapping function, and the number of hidden nodes doesn’t need to be given, which may avoid an extensive mapping calculation. The proposed framework contains two stages: spectral preprocessing and dimensionality reduction, as well as classification implementation with KELM. The experimental results on four datasets reveal that the radial basis function (RBF) should be a priority candidate kernel function for this kind of applications, and the suggestion of parameter selection is also given experimentally. Furthermore, the classification accuracy of KELM is superior to ELM in most cases, and KELM is significantly faster than ELM when the number of the training samples is relatively small, which is a common case for spectra data in chemometrics. Therefore, KELM may be a promising method for rapid spectra data classification.

It is the flourishing development of MOOCs that brings new mode of educa-tion. The paper focuses on the improvement of the teaching methods of pro-gramming experiment course in the environment of MOOCs, the develop-ment of experimental platform for innovative teaching methods, and the as-sessment methods of student learning. The current research and use of MOOCs are discussed, and its application in the programming experiment course. According to the present research analysis, the online programming experiment course platform is built, and a kind of online assessment system for students' achievement evaluation methods is elaborated. The academic performance and interest in learning are improved when experiment course platform is applied through two years teaching comparison. It is feasible to adopt the online platform for programming experiment course.

The bloom of Internet has made fast text categorization very essential. Generally, the popular methods have good classification accuracy but slow speed, and vice versa. This paper proposes a novel approach for fast text categorization, in which a collaborative work framework based on a linear classifier and an extreme learning machine (ELM) is constructed. The linear classifier, obtained by a modified non-negative matrix factorization algorithm, maps all documents from the original term space into the class space directly such that it performs classification very fast. The ELM, with good classification accuracy via some nonlinear and linear transformations, classifies a few of documents according to some given criteria to improve the classification quality of the total system. Experimental results show that the proposed method not only achieves good accuracy but also performs classification very fast, which improves the averaged speed by 180 % compared with its corresponding method.

The Latent Semantic Indexing (LSI) is a commonly used dimensionality reduction methods in text categorization; however, as a linear reconstructed method, its goal is to obtain the optimal representative feature rather than the optimal classication feature. This paper proposes a novel method in which the categorization information is combined into the latent semantic indexing to obtain more discriminating features than the standard latent semantic indexing. The experimental results show that the proposed method achieves good performance on two benchmark data sets, especially in the case where the dimensionality is greatly reduced.

This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.

Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms.

In order to satisfy the development of the cities and the requirements of modern factories, the network of water has been changing the pattern of the city quickly. It is possible to use automatic meter reading technology to read the data of the meters which were only read by human manual to solve the resource supply pipe network monitoring problem as the 2G/3G technology development. We designed a remote wireless automatic meter reading device to achieve the actual needs of users. STM32 works well as the core control devices to satisfy the requirements, combined with surveillance cameras and 3G wireless module to complete the hardware circuit design, which includes STM32 control circuit, sensor data acquisition part, 3G wireless module interface section, the analog video signal processing section and so on. The software was designed according to the users needs, the test results show that the device satisfies the requirements and could be used in the resource monitoring directly.

With the high accessibility of the social media, more and more people have been accustomed to sharing their personal contents across the social networks, which results in an explosive increase of data scale. In this study, in order to support information and knowledge discovery in big data, we propose an approach to aggregation and integration of personal big data from life logs in accordance with individual needs, which can benefit the sustainable information utilization process. In details, the organic stream, which is designed as an extensible data carrier, is introduced and developed to formulize and organize the personal big data, in order to extract dynamical individual needs from the tremendous amount of data posted through social media, and further aggregate and integrate the related data in a meaningful way, which can also facilitate the personalized information retrieval and reuse process. The architecture of the system with the foundational modules is given, and the experiment result is presented to demonstrate the usability and effectiveness of our approach.

We explore range expansion along an appointed direction using a fixed number of mobile nodes that do distributed multiple-input-multiple-output (MIMO) in the diversity configuration, also known as cooperative transmission (CT). The emphasis is on optimal groupings and locations of groups to maximize the multi-hop reach along a line; such information may inform the motion of robots that are deployed as far away as possible from a fixed base station yet still maintain connectivity. Also the impact of message-sharing within a cluster before relaying is considered. The simulation results show that for different fixed number of nodes, Rayleigh fading and arbitrary path loss exponent, there are preferred deployments depending on path loss exponent and sharing strategy.

The lasso regularization has successfully been used in regression models for feature selection; however, lasso considers all variable to be independent and noncorrelative, which will yield an excessively sparse solution (i.e., some important discriminating features might be discarded) if the features are highly correlated. This paper proposes a novel approach in which a sparse model was developed for text categorization. We firstly constructed a grouped structure according the correlation of text features, and then embedded the structure into a regression model via a between- and within- group sparse manner. The goal of such manner is that the groups containing many discriminating features can be selected even the features in these groups are highly correlated, and the noise within the selected groups could be discarded simultaneously, which is beneficial for classification. The experimental results show that the proposed method achieves a good tradeoff between performance and sparsity on three benchmark data sets.

It is one of the good solutions for improving network security to integrate many kinds of security techniques. Firewall and intrusion detection system can enforce security of the network effectively, but there are also drawbacks existing in themselves. Intrusion prevention system (IPS) is a technique combining the techniques of the firewall with that of the IDS properly. The characters of the IPS are introduced firstly in this paper, and then a scenario of IPS based network is described. Finally, a distributed IPS: IPS design based on SNMP is put forward, the function and implementation of each part of IPS is explained in detail.

Modularization of BCCDS (bornite craftworks creative design system) asks for data communications between functional modules of the system. This paper presents an improved communicating mechanism based on shared memory, expounding its design and algorithms, and applies it to BCCDS to build up a BCCDS oriented data communicating model. The efficiency of the communicating model is verified in the communication between two functional modules of BCCDS. Both methodology and case analysis prove that the data communicating model can preferably implement the data communications between functional modules of BCCDS and improve the efficiency of the bornite craftworks creative design.